This Markdown file is the first part of this analysis.

I use a unique dataset that contains information on 47.006 Airbnb listings from seven major German cities, namely Berlin, Munich, Hamburg, Cologne, Dresden, Stuttgart and Frankfurt am Main. Listings were gathered directly from Airbnb’s website in September 2017 using a custom web scraper. The dataset includes all publicly available information for a listing, including but not limited to prices, accommodation features, reviews and host details.

Data Preparations

print(paste0("Number of rows: ", dim(rooms)[1]))
## [1] "Number of rows: 47006"
print(paste0("Number of columns: ", dim(rooms)[2]))
## [1] "Number of columns: 62"
str(rooms)
## Classes 'tbl_df', 'tbl' and 'data.frame':    47006 obs. of  62 variables:
##  $ room_id                   : int  19117409 5728058 19954984 9918551 13836114 20355318 18732461 12021779 18019626 20121368 ...
##  $ host_id                   : int  133588182 333588 140968262 50992051 81617924 80225160 49157795 7901771 2307050 20759906 ...
##  $ room_type                 : chr  "Entire home/apt" "Entire home/apt" "Entire home/apt" "Entire home/apt" ...
##  $ country                   : chr  "Deutschland" "Deutschland" "Deutschland" "Deutschland" ...
##  $ city                      : chr  "Hamburg" "Hamburg" "München" "Schönefeld" ...
##  $ neighborhood              : chr  NA NA NA NA ...
##  $ address                   : chr  "Othmarschen, Hamburg" "Neustadt, Hamburg" "Schwabing - West, München" "Schönefeld" ...
##  $ price                     : int  129 116 91 43 61 49 120 120 145 91 ...
##  $ nightly_price             : int  129 116 91 43 61 49 120 120 145 91 ...
##  $ reviews                   : int  3 24 10 0 13 1 10 11 4 1 ...
##  $ accommodates              : int  2 2 6 1 2 2 5 6 5 4 ...
##  $ bathrooms                 : int  1 1 1 1 1 1 1 1 2 1 ...
##  $ bedrooms                  : int  1 1 2 0 1 1 3 2 3 1 ...
##  $ bed_type                  : chr  "Real Bed" "Real Bed" "Real Bed" "Real Bed" ...
##  $ minstay                   : int  2 3 2 3 1 2 2 2 6 2 ...
##  $ last_modified             : POSIXct, format: "2017-09-27 08:47:10" "2017-09-27 08:47:27" ...
##  $ latitude                  : num  53.6 53.6 48.2 52.4 53.6 ...
##  $ longitude                 : num  9.9 9.98 11.56 13.44 9.98 ...
##  $ survey_id                 : int  7 7 2 1 7 3 7 2 1 2 ...
##  $ location                  : chr  NA NA NA NA ...
##  $ coworker_hosted           : chr  NA NA NA NA ...
##  $ extra_host_languages      : chr  "{en}" "{en}" "{en}" "{en,fr}" ...
##  $ name                      : chr  "Komfortable Erdgeschosswohnung mit Südterrasse." "Cozy city apartment - very central" "EmiLi - Helle, gemütliche Wohnung in bester Lage" "Einliegerwohnung auf dem Mauerweg" ...
##  $ property_type             : chr  "Wohnung" "Wohnung" "Wohnung" "Bed & Breakfast" ...
##  $ currency                  : chr  "EUR" "EUR" "EUR" "EUR" ...
##  $ rate_type                 : chr  "nightly" "nightly" "nightly" "nightly" ...
##  $ overall_satisfaction      : chr  "100" "96" "100" NA ...
##  $ cleanliness_satisfaction  : int  10 10 10 NA 10 10 10 9 10 8 ...
##  $ communication_satisfaction: int  10 10 10 NA 10 10 10 9 10 6 ...
##  $ location_satisfaction     : int  10 10 10 NA 10 10 10 9 10 8 ...
##  $ accuracy_satisfaction     : int  9 10 10 NA 10 10 10 9 10 10 ...
##  $ checkin_satisfaction      : int  10 10 10 NA 10 10 10 10 10 6 ...
##  $ value_satisfaction        : chr  "10" "10" "10" NA ...
##  $ amenities                 : chr  "{128,1,129,4,8,9,21,91,92,93,30,94,31,95,96,33,98,35,99,100,101,40,41,44,110,111,112,113,50,115,116,120,57,121,61,127}" "{1,49,50,35,8,40,28,44,45,30,46}" "{33,129,35,4,38,8,40,73,42,44,45,46,47,28,61,30}" "{33,34,35,4,37,38,39,8,40,9,41,44,45,46,47,16,49,28,30,31}" ...
##  $ cancel_policy             : chr  "4" "5" "3" "3" ...
##  $ instant_book              : chr  "false" "false" "true" "false" ...
##  $ response_time             : chr  "51118" "1000" "1" "28566" ...
##  $ response_rate             : num  1 1 1 1 1 0.5 1 1 1 1 ...
##  $ friend_count              : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ wishlist_count            : int  14 90 27 17 26 0 43 73 20 4 ...
##  $ pic_count                 : chr  "12" "4" "7" "5" ...
##  $ superhost                 : chr  "false" "false" "false" "false" ...
##  $ description_language      : chr  "de" "de" "de" "de" ...
##  $ hostname                  : chr  "Michael" "Nana" "Lina & Emily" "Liliana" ...
##  $ rule_children             : chr  "true" "false" "true" "true" ...
##  $ rule_infants              : chr  "false" "false" "false" "true" ...
##  $ rule_pets                 : chr  "false" "false" "false" "false" ...
##  $ rule_smoking              : chr  "false" "false" "false" "false" ...
##  $ rule_events               : chr  "false" "false" "false" "false" ...
##  $ hostprofilepic            : chr  "https://a0.muscache.com/im/pictures/7e75a61b-5240-4867-b496-f7efdb564053.jpg?aki_policy=profile_x_medium" "https://a0.muscache.com/im/users/333588/profile_pic/1406487683/original.jpg?aki_policy=profile_x_medium" "https://a0.muscache.com/im/pictures/02b39cd9-1fd4-498e-b830-203f11919ee2.jpg?aki_policy=profile_x_medium" "https://a0.muscache.com/im/pictures/46deaa24-5700-45ed-b0ee-7a81b552da7f.jpg?aki_policy=profile_x_medium" ...
##  $ cleaning_fee              : chr  "20" NA NA NA ...
##  $ security_deposit          : chr  NA NA NA NA ...
##  $ last_review               : POSIXct, format: "2017-09-09 13:37:58" "2017-06-18 11:33:06" ...
##  $ positive_reviews          : POSIXct, format: NA NA ...
##  $ negative_reviews          : Date, format: NA NA ...
##  $ last_cal_update           : chr  "2017-06-22" "2017-09-18" "2017-09-04" "2017-09-20" ...
##  $ member_since              : chr  "Juni 2017" "Januar 2011" "Juli 2017" "Dezember 2015" ...
##  $ host_verified             : chr  "TRUE" "TRUE" "FALSE" "FALSE" ...
##  $ deleted                   : chr  "0" "0" "0" "0" ...
##  $ filled                    : chr  "TRUE" "TRUE" "TRUE" "TRUE" ...
##  $ description               : chr  "Die 80 qm große Wohnung ist im Erdgeschoß gelegen und sehr gut ausgestattet. Es gibt eine moderne Küche mit Ess"| __truncated__ "Bright, quiet, fully furnished, in the middle of Hamburg – great central suburb „Neustadt“. Fully equipped + li"| __truncated__ "Super schöne, sehr helle Wohnung. Stilvoll und mit viel Liebe eingerichtet. In top Lage!  Karstadt, Rewe, Lidl,"| __truncated__ "- sehr ruhige Lage im Süden Berlins; 150 m zum Bus - besteht aus einem Zimmer (23,10 qm) mit integrierter Küche"| __truncated__ ...
##  $ base_price                : chr  NA NA NA NA ...
##  - attr(*, "spec")=List of 2
##   ..$ cols   :List of 62
##   .. ..$ room_id                   : list()
##   .. .. ..- attr(*, "class")= chr  "collector_integer" "collector"
##   .. ..$ host_id                   : list()
##   .. .. ..- attr(*, "class")= chr  "collector_integer" "collector"
##   .. ..$ room_type                 : list()
##   .. .. ..- attr(*, "class")= chr  "collector_character" "collector"
##   .. ..$ country                   : list()
##   .. .. ..- attr(*, "class")= chr  "collector_character" "collector"
##   .. ..$ city                      : list()
##   .. .. ..- attr(*, "class")= chr  "collector_character" "collector"
##   .. ..$ neighborhood              : list()
##   .. .. ..- attr(*, "class")= chr  "collector_character" "collector"
##   .. ..$ address                   : list()
##   .. .. ..- attr(*, "class")= chr  "collector_character" "collector"
##   .. ..$ price                     : list()
##   .. .. ..- attr(*, "class")= chr  "collector_integer" "collector"
##   .. ..$ nightly_price             : list()
##   .. .. ..- attr(*, "class")= chr  "collector_integer" "collector"
##   .. ..$ reviews                   : list()
##   .. .. ..- attr(*, "class")= chr  "collector_integer" "collector"
##   .. ..$ accommodates              : list()
##   .. .. ..- attr(*, "class")= chr  "collector_integer" "collector"
##   .. ..$ bathrooms                 : list()
##   .. .. ..- attr(*, "class")= chr  "collector_integer" "collector"
##   .. ..$ bedrooms                  : list()
##   .. .. ..- attr(*, "class")= chr  "collector_integer" "collector"
##   .. ..$ bed_type                  : list()
##   .. .. ..- attr(*, "class")= chr  "collector_character" "collector"
##   .. ..$ minstay                   : list()
##   .. .. ..- attr(*, "class")= chr  "collector_integer" "collector"
##   .. ..$ last_modified             :List of 1
##   .. .. ..$ format: chr ""
##   .. .. ..- attr(*, "class")= chr  "collector_datetime" "collector"
##   .. ..$ latitude                  : list()
##   .. .. ..- attr(*, "class")= chr  "collector_double" "collector"
##   .. ..$ longitude                 : list()
##   .. .. ..- attr(*, "class")= chr  "collector_double" "collector"
##   .. ..$ survey_id                 : list()
##   .. .. ..- attr(*, "class")= chr  "collector_integer" "collector"
##   .. ..$ location                  : list()
##   .. .. ..- attr(*, "class")= chr  "collector_character" "collector"
##   .. ..$ coworker_hosted           : list()
##   .. .. ..- attr(*, "class")= chr  "collector_character" "collector"
##   .. ..$ extra_host_languages      : list()
##   .. .. ..- attr(*, "class")= chr  "collector_character" "collector"
##   .. ..$ name                      : list()
##   .. .. ..- attr(*, "class")= chr  "collector_character" "collector"
##   .. ..$ property_type             : list()
##   .. .. ..- attr(*, "class")= chr  "collector_character" "collector"
##   .. ..$ currency                  : list()
##   .. .. ..- attr(*, "class")= chr  "collector_character" "collector"
##   .. ..$ rate_type                 : list()
##   .. .. ..- attr(*, "class")= chr  "collector_character" "collector"
##   .. ..$ overall_satisfaction      : list()
##   .. .. ..- attr(*, "class")= chr  "collector_character" "collector"
##   .. ..$ cleanliness_satisfaction  : list()
##   .. .. ..- attr(*, "class")= chr  "collector_integer" "collector"
##   .. ..$ communication_satisfaction: list()
##   .. .. ..- attr(*, "class")= chr  "collector_integer" "collector"
##   .. ..$ location_satisfaction     : list()
##   .. .. ..- attr(*, "class")= chr  "collector_integer" "collector"
##   .. ..$ accuracy_satisfaction     : list()
##   .. .. ..- attr(*, "class")= chr  "collector_integer" "collector"
##   .. ..$ checkin_satisfaction      : list()
##   .. .. ..- attr(*, "class")= chr  "collector_integer" "collector"
##   .. ..$ value_satisfaction        : list()
##   .. .. ..- attr(*, "class")= chr  "collector_character" "collector"
##   .. ..$ amenities                 : list()
##   .. .. ..- attr(*, "class")= chr  "collector_character" "collector"
##   .. ..$ cancel_policy             : list()
##   .. .. ..- attr(*, "class")= chr  "collector_character" "collector"
##   .. ..$ instant_book              : list()
##   .. .. ..- attr(*, "class")= chr  "collector_character" "collector"
##   .. ..$ response_time             : list()
##   .. .. ..- attr(*, "class")= chr  "collector_character" "collector"
##   .. ..$ response_rate             : list()
##   .. .. ..- attr(*, "class")= chr  "collector_double" "collector"
##   .. ..$ friend_count              : list()
##   .. .. ..- attr(*, "class")= chr  "collector_integer" "collector"
##   .. ..$ wishlist_count            : list()
##   .. .. ..- attr(*, "class")= chr  "collector_integer" "collector"
##   .. ..$ pic_count                 : list()
##   .. .. ..- attr(*, "class")= chr  "collector_character" "collector"
##   .. ..$ superhost                 : list()
##   .. .. ..- attr(*, "class")= chr  "collector_character" "collector"
##   .. ..$ description_language      : list()
##   .. .. ..- attr(*, "class")= chr  "collector_character" "collector"
##   .. ..$ hostname                  : list()
##   .. .. ..- attr(*, "class")= chr  "collector_character" "collector"
##   .. ..$ rule_children             : list()
##   .. .. ..- attr(*, "class")= chr  "collector_character" "collector"
##   .. ..$ rule_infants              : list()
##   .. .. ..- attr(*, "class")= chr  "collector_character" "collector"
##   .. ..$ rule_pets                 : list()
##   .. .. ..- attr(*, "class")= chr  "collector_character" "collector"
##   .. ..$ rule_smoking              : list()
##   .. .. ..- attr(*, "class")= chr  "collector_character" "collector"
##   .. ..$ rule_events               : list()
##   .. .. ..- attr(*, "class")= chr  "collector_character" "collector"
##   .. ..$ hostprofilepic            : list()
##   .. .. ..- attr(*, "class")= chr  "collector_character" "collector"
##   .. ..$ cleaning_fee              : list()
##   .. .. ..- attr(*, "class")= chr  "collector_character" "collector"
##   .. ..$ security_deposit          : list()
##   .. .. ..- attr(*, "class")= chr  "collector_character" "collector"
##   .. ..$ last_review               :List of 1
##   .. .. ..$ format: chr ""
##   .. .. ..- attr(*, "class")= chr  "collector_datetime" "collector"
##   .. ..$ positive_reviews          :List of 1
##   .. .. ..$ format: chr ""
##   .. .. ..- attr(*, "class")= chr  "collector_datetime" "collector"
##   .. ..$ negative_reviews          :List of 1
##   .. .. ..$ format: chr ""
##   .. .. ..- attr(*, "class")= chr  "collector_date" "collector"
##   .. ..$ last_cal_update           : list()
##   .. .. ..- attr(*, "class")= chr  "collector_character" "collector"
##   .. ..$ member_since              : list()
##   .. .. ..- attr(*, "class")= chr  "collector_character" "collector"
##   .. ..$ host_verified             : list()
##   .. .. ..- attr(*, "class")= chr  "collector_character" "collector"
##   .. ..$ deleted                   : list()
##   .. .. ..- attr(*, "class")= chr  "collector_character" "collector"
##   .. ..$ filled                    : list()
##   .. .. ..- attr(*, "class")= chr  "collector_character" "collector"
##   .. ..$ description               : list()
##   .. .. ..- attr(*, "class")= chr  "collector_character" "collector"
##   .. ..$ base_price                : list()
##   .. .. ..- attr(*, "class")= chr  "collector_character" "collector"
##   ..$ default: list()
##   .. ..- attr(*, "class")= chr  "collector_guess" "collector"
##   ..- attr(*, "class")= chr "col_spec"
# Convert strings to numeric
rooms <- rooms %>% 
  mutate(overall_satisfaction = as.numeric(overall_satisfaction),
         pic_count = as.numeric(pic_count)) %>%
  filter(!is.na(overall_satisfaction))

(1) Cities

Keep listings from the following cities: Hamburg, München, hamburg, Köln, FFM, Dresden, Stuttgart

## create clean-up function
create_city <- function(x, city){
  city_clean <- ifelse(grepl(x, city),x , city) 
  return(city_clean)
}
city_list <- c("Hamburg","München","Berlin","Frankfurt","Köln","Stuttgart","Dresden")

for(i in city_list){
  rooms$city <- create_city(i, rooms$city)
}

rooms %>%
  filter(city %in% city_list) -> rooms

rooms %>%
  group_by(city) %>%
  tally() %>%
  ggplot(aes(reorder(city, n, desc),n)) +
  geom_col(fill = col[3], alpha = 0.8) +
  labs(x="", y="", title="Count")

(2) Property Type

rooms %>%
  group_by(property_type) %>%
  tally() %>%
  ggplot(aes(reorder(property_type, n),n)) +
  geom_col(fill = col[3], alpha = 0.8) +
  labs(x="", y="", title="Property Types") +
  coord_flip()

To keep things simple, I will just keep listings of property type “Wohnung” (apartment)

rooms %>%
  filter(property_type == "Wohnung") -> rooms

(3) Roomtype

rooms %>%
  ggplot(aes(room_type)) +
  geom_bar(fill = col[3], alpha = 0.8) +
  labs(x="", y="")

(4) Price

rooms %>%
  ggplot(aes(city, price)) +
  geom_boxplot(outlier.size = 0)

Apparently, there are some outliers. After cheking the respective listings, I decided to exclude them.

rooms %>%
  filter(price < 1500) -> rooms
rooms$price.cut <- cut(rooms$price, c(seq(0,500,1), Inf))

rooms %>%
  ggplot(aes(as.numeric(price.cut), factor(city))) +
  geom_density_ridges(scale = 5,
                      fill = col[3], alpha = 0.7,
                      color = "white") +
  theme_ridges() +
  scale_x_continuous(expand = c(0, 0), labels = c(seq(0,400,100),">500")) +
  labs(y="", x="Price")

(5) Rating

rooms %>%
  ggplot(aes(overall_satisfaction, factor(room_type))) +
  geom_density_ridges(scale = 5,
                      fill = col[3], alpha = 0.7,
                      color = "white") +
  scale_x_continuous(expand = c(0, 0)) +
  labs(y="", x="Rating")

(6) Number of Reviews

Next, I exclude listings with less than three reviews, as it can be assumed that these listings have never been booked, or only very little.

rooms %>% 
  filter(reviews >= 3) -> rooms
rooms$reviews.cut <- cut(rooms$reviews, c(seq(0,50,1), Inf))

rooms %>%
  ggplot(aes(as.numeric(reviews.cut), factor(city))) +
  geom_density_ridges(scale = 5,
                      fill = col[3], alpha = 0.7,
                      color = "white") +
  scale_y_discrete(expand = c(0,0)) +
  scale_x_continuous(expand = c(0,0),
                     breaks = c(seq(0,50,10)),
                     labels = c(seq(0,40,10),">50")) +
  labs(y="", x="Number of Reviews")

Final dataframe

df <- rooms %>% 
  select(room_id, name, 
         description, city, price, overall_satisfaction,
         room_type, bed_type, pic_count,
         reviews, accommodates, bedrooms, minstay,
         latitude, longitude) %>%
  mutate(fulltext = paste(name, description, sep=" "))

Textdata

Turning to the text data, lets first have a quick look at three random descriptions:

rooms %>% sample_n(3) %>%
  select(description) %>%
  knitr::kable(align = "l")
description
I offer a very cozy room with a double bed, a couch and a Desk, 10 Min from Central Station. Wifi is available Es ist ein hübsches Zimmer mit einem Doppelbett, einer Couch und einem Schreibtisch, 10 Minuten vom Hauptbahnhof. Wifi gibts auch
Unser 1-Zimmer Nichtraucher-Appartment mit ca. 28qm befindet sich in einem ruhigen Mehrfamilienhaus in Frankfurt Hausen unmittelbar der U-Bahn Haltestelle "Große Nelkenstraße". Ideal für Besucher sowie Geschäftsreisende, die eine schnelle Anbindung Richtung Innenstadt/Messe möchten. Abholung am Flughafen möglich; wir sind absolut zuverlässig.Das Appartment ist vollständig neu renoviert (Laminat/Fließen) und neu eingerichtet mit einem großen Doppelbett, Schreibtisch, Kochgelegenheit sowie selbstverständlich eigenes Bad/WC/Dusche. Selbstverständlich HD-TV sowie high-speed W-LAN (50.000) und ein gefüllter Kühlschrank inklusive. Mindestmietdauer 5 Tage. Endreinigung 49,- EUR Einkaufsgelegenheiten in fußläufiger Entfernung, Parkplätze problemlos.Sonderpreise für Aufenthalte von über einer Woche bis zu einem Monat (ausserhalb der Messezeiten) auf Anfrage.Absolut privat und abgeschlossen.
Altbau-Wohnung im Herzen Hamburgs. 50qm, max. 3 Personen. 1 Doppelbett - 210cm x 160cm PERFEKT FÜR LANGE LULATSCHE UND ALLE DIE PLATZ BRAUCHEN, 1 Wohnzimmercouch 160cm. Badezimmer mit Dusche, Küche mit Spülmaschine, geräumiges Wohnzimmer, Wifi.

Languages

In which languages are the descriptions written?

load(file = "../output/prep1.Rda")
df %>% group_by(language) %>% 
  tally() %>%
  ggplot(aes(reorder(language, n),n)) +
  geom_col(fill = col[3], alpha = 0.7) +
  coord_flip() +
  labs(x="",y="")

Check sample articles if the classification is valid

df %>%
  sample_n(5) %>%
  select(fulltext, language) %>%
  knitr::kable()
fulltext language
Charmantes Zimmer im Herzen Schwabings Die Unterkunft befindet sich in einer der schönsten Ecken Schwabings, Restaurants, Cafes, Geschäfte direkt ums Eck, die Münchner Freiheit mit bester Verkehrsanbindung (U-Bahn, Straßenbahn, Bus) weniger als 5 Min zu Fuß entfernt. In die Innenstadt bzw. zum Marienplatz braucht man mit der U-Bahn nur ca. 10 Minuten, der Englische Garten ist auch nur wenige Minuten entfernt. Das Zimmer ist Teil einer schönen Altbauwohnung, deren Bad und Küche Du mit mir teilst. german
Weddinger Paradies Die Ideale Unterkunft für alle alter! Liegt zentral in Weddingerkiez mit vielfälltige Einkaufs,Transport und vergnügen Gelegenheit. Neurenovierte altbau Wohnung mit genügend Fläche zum loslassen !! Die Wohnung liegt im Hochparterre des Hauses! german
Belle Epoque Apartment in Berlin Welcome in one of the most autentic flat in Berlin. A small door will you bring back directly in the 20s years of the Berlin s “Belle epoque”. Old stripped pine floorboards, spacious high ceilings in traditional Berlin style, a big classic sofa of the 20s and a tester bed have been put together to create an unique atmosfeare of a forgotten time. The kitchen (with all things that are necessary for cooking and eating) and the bathroom (with a bathtub) have been also renewed following this style. english
3 Room Apartment central Berlin for family Zentral gelegene Wohnung, 5 min bis zum U-Bahnhof (Nauener Platz), sehr hell und perfekt für Familien eingerichtet. Wir haben eine 3 jährige und eine 1,5 jährige Tochter. This apartment is in central Berlin, close to the underground railway. We live here with our two children (3 years and 1.5 years) and everything is at hand such as the highchairs, the babybed, books, toys and so on. english
Im Herzen Schwabings! Wohnen im ruhigen Teil des In-Viertels Schwabing! Unsere Wohnung liegt in unmittelbarer Nähe des Luitpold- & Olympiaparks. U-Bahn, Tram & Bus 200 m entfernt. In nur 5 Minuten erreicht man den Hauptbahnhof. Herzlich Willkommen in Schwabing! middle_frisian

Ok, looks good. Lets only keep listings with german and english descriptions.

df %>%
  filter(language %in% c("german","english")) -> df
ggplot(df, aes(x=factor(city))) +
  geom_bar(aes(fill = language),
           alpha = 0.8) +
  labs(x="", y="", fill="")

It is not surprising that Berlin seems to be the most international city, measured by the listings that have their description in English. But I am a little disappointed with Hamburg…

Word count

How long are the descriptions on average?

df$text_length <- sapply(gregexpr("\\S+", df$fulltext), length)
df$text_length.cut <- cut(df$text_length, c(seq(0,150,1),Inf))

df %>%
  ggplot(aes(as.numeric(text_length.cut), factor(city))) +
  geom_density_ridges(aes(fill = language),
                      color = "white", alpha = 0.8) +
  scale_x_continuous(expand = c(0,0), 
                     labels = c(seq(0,100,50),">150")) +
  labs(y = "", x = "Word Count", fill= "") +
  theme()

Surprisingly, the English texts are longer.

Pre-Processsing

Next, I have to pre-process the text data to be able to include it into my model. Text data is inherently high-dimensional, so to reduce this dimensionality the following steps will be applied:

  1. Remove Punctuation, Numbers,…
  2. Stopword removal: Stopwords (highly frequent terms like “and”, “or”, “the”) are stripped out of text as they do add any helpfull information about the listing.
  3. Tokenization: splitting of a raw character string into individual elements of interest: words, numbers, punctuation.
  4. Document Term Matrix Represent each listing as a numerical array of unique terms (bag-of-words model). This will be done in part three of this project.

(1) Remove Punctuation, Numbers, …

df$text_cleaned <- gsub("[[:punct:]]", " ", df$fulltext)
df$text_cleaned <- gsub("[[:cntrl:]]", " ", df$text_cleaned)
df$text_cleaned <- gsub("[[:digit:]]", " ", df$text_cleaned)
df$text_cleaned <- gsub("^[[:space:]]+", " ", df$text_cleaned)
df$text_cleaned <- gsub("[[:space:]]+$", " ", df$text_cleaned)
df$text_cleaned <- tolower(df$text_cleaned)

(2) Remove Stopwords

df$text_cleaned <- removeWords(df$text_cleaned, stopwords("english"))
df$text_cleaned <- removeWords(df$text_cleaned, stopwords("german"))

(3) Tokenizing

Unigrams

token.df <- df %>%
  tidytext::unnest_tokens(word, text_cleaned) %>%
  filter(nchar(word) > 1) %>%
  filter(nchar(word) < 30)

token.df %>% 
  count(word, sort = TRUE) %>%
  ungroup() %>%
  top_n(20, n) %>%
  knitr::kable(align="l")
word n
wohnung 12264
apartment 9732
zimmer 8800
room 8529
min 8365
berlin 5994
bahn 5187
restaurants 4511
minuten 4289
flat 4200
küche 3877
city 3862
nähe 3800
unterkunft 3488
bars 3228
qm 3060
direkt 2992
liegt 2983
station 2955
lage 2916

Bigrams

bigram.df <- df %>%
  unnest_tokens(bigram, text_cleaned, 
                          token = "ngrams", n=2) 

bigram.df %>% 
  count(bigram, sort = TRUE) %>%
  ungroup() %>%
  top_n(20, n) %>%
  knitr::kable(align="l")
bigram n
u bahn 2699
s bahn 1870
zimmer wohnung 1497
wohnung liegt 1287
prenzlauer berg 1083
living room 1081
city center 989
walking distance 982
unterkunft gut 936
bars restaurants 891
paare alleinreisende 848
gut paare 832
unterkunft nähe 811
restaurants bars 786
alleinreisende abenteurer 771
wohnung befindet 751
unmittelbarer nähe 745
unterkunft lieben 733
st pauli 689
lieben wegen 678

Wordclouds

corp <- corpus(df$text_cleaned)
docvars(corp)<-df$city   #attaching the class labels to the corpus message text

col <- RColorBrewer::brewer.pal(10, "BrBG")

(1) Berlin

c.plot <- corpus_subset(corp, docvar1=="Berlin")
c.plot<-dfm(c.plot, tolower = TRUE, remove_numbers = TRUE, remove=stopwords("SMART"))

textplot_wordcloud(c.plot, min.freq = 250, color = col)

(2) Hamburg

c.plot <- corpus_subset(corp, docvar1=="Hamburg")
c.plot<-dfm(c.plot, tolower = TRUE, remove_numbers = TRUE, remove=stopwords("SMART"))

textplot_wordcloud(c.plot, min.freq = 200, color = col)

(3) München

c.plot <- corpus_subset(corp, docvar1=="München")
c.plot<-dfm(c.plot, tolower = TRUE, remove_numbers = TRUE, remove=stopwords("SMART"))

textplot_wordcloud(c.plot, min.freq = 50, color = col)

(4) Köln

c.plot <- corpus_subset(corp, docvar1=="Köln")
c.plot<-dfm(c.plot, tolower = TRUE, remove_numbers = TRUE, remove=stopwords("SMART"))

textplot_wordcloud(c.plot, min.freq = 50, color = col)

(5) Frankfurt

c.plot <- corpus_subset(corp, docvar1=="Frankfurt")
c.plot<-dfm(c.plot, tolower = TRUE, remove_numbers = TRUE, remove=stopwords("SMART"))

textplot_wordcloud(c.plot, min.freq = 50, color = col)

(6) Stuttgart

c.plot <- corpus_subset(corp, docvar1=="Stuttgart")
c.plot<-dfm(c.plot, tolower = TRUE, remove_numbers = TRUE, remove=stopwords("SMART"))

textplot_wordcloud(c.plot, min.freq = 50, color = col)

(7) Dresden

c.plot <- corpus_subset(corp, docvar1=="Dresden")
c.plot<-dfm(c.plot, tolower = TRUE, remove_numbers = TRUE, remove=stopwords("SMART"))

textplot_wordcloud(c.plot, min.freq = 50, color = col)

Go to Part 2: or go back to the overview